Coronary artery disease (CAD) is one of the top killer diseases in today's society, accounting for nearly 500,000 deaths in America each year. Studies estimate that 50% of men and 33% of women under the age of 40 will develop some form of CAD sometime during their lifetimes. Sudden cardiac death has steadily accounted for approximately 50% of all heart-related, out-of-hospital deaths and improved clinical procedures almost entirely ignore this group. The fact that patients generally fail to recognize their symptoms and seek prompt medical attention contributes to this tragic statistic. However, the early stages of CAD are usually non-symptomatic and even invisible with current clinical cardiac signal analysis strategies. If fatal cardiac arrhythmia events can be sensitively and accurately detected and captured, the cardiac functional abnormality frequency and severity can be earlier detected and characterized. This may greatly help the doctor to provide early and more effective clinical treatment and then prevent fatal heart diseases.
Recognition and characterization of early arrhythmia events, such as ventricular tachycardia, myocardial ischemia (MI) and infarction, are critical for rhythm management of the cardiac disorders and irregularities. Currently, waveform morphology and time domain parameter analysis of depolarization and repolarization of cardiac signals, such as P wave, QRS complex, ST segment and T wave, are used for monitoring and identifying cardiac arrhythmia. However, such current clinical methodologies are sometimes subjective and time-consuming, and require expertise and clinical experience for accurate interpretation and proper cardiac rhythm management.
Recently, some research efforts have started to apply more sophisticated mathematical theories to interpret patient signal, such as frequency analysis, symbolic complexity analysis and nonlinear entropy evaluation, focusing primarily on generating a new pathology index for qualitative cardiac arrhythmia characterization. Although these research efforts may be able to qualitatively describe the pathology or event, they cannot provide enough information on cardiac electrophysiological function/activity interpretation, tissue mapping, arrhythmia localization, etc.
Additionally, traditional medical methods usually focus on changes in the time (amplitude, latency, etc.) or frequency (power, spectrum, etc.) domain, which may not efficiently and accurately capture minute signal changes in a partial portion (P wave, QRS complex, ST segment, etc.) of a cardiac cycle. Such changes are usually invisible in the signal wave morphology display or need extensive clinical expertise to obtain an accurate diagnosis. Consequently, a high failure rate of arrhythmia diagnosis and false negatives (FN) may result. A false negative (FN) wrongly indicates that a person has disease X (e.g., myocardial ischemia), even though he or she does not actually have the disease.
Known clinical approaches for cardiac arrhythmia identification and analysis based on electrocardiography (ECG) signals are subjective and require extensive expertise and clinical experience for accurate interpretation and appropriate cardiac rhythm management. More objective analytical and diagnostic strategies for cardiac signals and activities are needed. Furthermore, known methods based on amplitude (or voltage) variation analysis may not be sufficient for cardiac function evaluation and pathology diagnosis. No quantitative clinical evaluation and link between myocardial ischemia event/status and the amplitude and variation index are provided.
Known clinical evaluations for cardiac malfunction detection and characterization (e.g., MI and infarction) are based on the “golden standard” using ST segment voltage deviation (e.g., 0.1 mV elevation is the clinical standard for myocardial ischemia or MI detection). However, there are at least two shortcomings with the golden standard for this kind of diagnosis and evaluation: (a) this standard only works for surface ECG signals, but not for intra-cardiac electrograms (ICEG); (b) ST segment deviation in voltage cannot be utilized to quantitatively characterize myocardial ischemia severity. There is a clinical need for both quantitative and qualitative approaches for cardiac arrhythmia detection and characterization.
Further, current clinical methods based on cardiac pathology information extraction (e.g., atrial fibrillation or AF detection and MI diagnosis) may not be able to qualitatively and quantitatively capture minute changes, and predict the pathological trend, especially in the early stage of tissue malfunctioning and acute cardiac arrhythmia. Known current methods may not efficiently analyze the real-time growing trend of cardiac arrhythmias, such as the pathology trend from low risk to medium risk, and then to high risk (severe and fatal) rhythm (especially for some fatal arrhythmia, such as VT growing and trend to VF).
Even further, known clinical methods and strategies for cardiac pathology event detection and evaluations rely only on partial portion cardiac waveform and electrophysiological procedure, such as ST segment changes for myocardial ischemia. However, different portions of the cardiac waveform may provide some additional information of the events and cardiac diseases.
In addition, there are many cardiac arrhythmia (such as ischemia and fibrillation) analysis methods for detecting and treating heart pathology, such as heart rate variability, medicine, implantable cardioverter, etc. However, the efficiency and reliability of these kinds of clinical approaches may not be sufficient, especially in a noisy environment since atrial activities may be buried in noise and artifacts. It would be a challenge to efficiently and reliably extract atrial arrhythmia information from the electrophysiological signals (e.g., surface ECG and intra-cardiac electrograms). Current medical applications also need better methods to detect cardiac pathology events in a more reliable and timely manner, and particularly to provide early warning and treatment options for fatal heart arrhythmia, which can be used in implantable cardioverter defibrillator (ICD) patients.